“Artifactual” arsenate DNA

The recent claim by Wolfe-Simon et al. that the Halomonas bacterial strain GFAJ-1 when grown in arsenate-containing medium with limiting phosphate is able to substitute phosphate with arsenate in biomolecules including nucleic acids and in particular DNA1 arose much skepticism, primarily due to the very limited chemical stability of arsenate esters (see ref. 2 and references therein). A major part of the criticisms was concerned with the insufficient (bio)chemical evidence in the Wolfe-Simon study for the actual chemical incorporation of arsenate in DNA (and/or RNA). Redfield et al. now present evidence that the identification of arsenate DNA was artifactual

The impact of stochastic lead times on the bullwhip effect–a theoretical insight

In this article, we analyze the models quantifying the bullwhip effect in supply chains with stochastic lead times and find advantages and disadvantages of their approaches to the bullwhip problem. Moreover, using computer simulation, we find interesting insights into the bullwhip behavior for a particular instance of a multi-echelon supply chain with constant customer demands and random lead times. We confirm the recent finding of Michna and Nielsen that under certain circumstances lead time signal processing is by itself a fundamental cause of bullwhip effect just like demand-signal processing is. The simulation also shows that in this supply chain the delay parameter of demand forecasting smooths the bullwhip effect at the manufacturer level much faster than the delay parameter of lead time forecasting. Additionally, in the supply chain with random demands, the reverse behavior is observed, that is, the delay parameter of lead time forecasting smooths bullwhip effect at the retailer stage much faster than the delay parameter of demand forecasting. At the manufacturer level, the delay parameter of demand forecasting and the delay parameter of lead time forecasting dampen the effect with a similar strength

Detection of intercalation-induced changes in DNA structure by reaction with diethyl pyrocarbonate or potassium permanganate Evidence against the induction of Hoogsteen base pairing by echinomycin

AbstractBinding of the bis-intercalators echinomycin and N,N′-di(9-acridinyl)spermidine or the mono-intercalators 9-aminoacridine and ethidium to DNA induces hypersensitivity of adenines towards reaction with diethyl pyrocarbonate. It is proposed that this hyperreactivity is due to the DNA helix unwinding and extension induced by intercalation, thereby exposing N7 in the major groove, and not as previously suggested to the formation of Hoogsteen base pairing. Hypersensitivity of thymines towards oxidation with permanganate is also induced upon binding of these drugs (especially the bis-intercalators) to DNA. This thymine hyperreactivity is both sequence- and intercalator-dependent, thereby indicating the potential of KMnO4 as a useful probe for analysing the structure of intercalator-DNA complexes in solution

Entanglement in Anderson Nanoclusters

We investigate the two-particle spin entanglement in magnetic nanoclusters described by the periodic Anderson model. An entanglement phase diagram is obtained, providing a novel perspective on a central property of magnetic nanoclusters, namely the temperature dependent competition between local Kondo screening and nonlocal Ruderman-Kittel-Kasuya-Yoshida spin ordering. We find that multiparticle entangled states are present for finite magnetic field as well as in the mixed valence regime and away from half filling. Our results emphasize the role of charge fluctuations.Comment: 5 pages, 3 figure

Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise

This paper shows how to use realised kernels to carry out efficient feasible inference on the ex-post variation of underlying equity prices in the presence of simple models of market frictions. The issue is subtle with only estimators which have symmetric weights delivering consistent estimators with mixed Gaussian limit theorems. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem. Realised kernels can also be selected to (i) be analysed using endogenously spaced data such as that in databases on transactions, (ii) allow for market frictions which are endogenous, (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.Bipower variation, Long run variance estimator, Market frictions, Quadratic variation, Realised variance

Subsampling realised kernels

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work.Bipower variation; Long run variance estimator; Market frictions; Quadratic variation; Realised kernel; Realised variance; Subsampling.

Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise

We consider kernel-based estimators of integrated variances in the presence of independent market microstructure effects. We derive the bias and variance properties for all regular kernel-based estimators and derive a lower bound for their asymptotic variance. Further we show that the subsample-based estimator is closely related to a Bartlett-type kernel estimator. The small difference between the two estimators due to end effects, turns out to be key for the consistency of the subsampling estimator. This observation leads us to a modified class of kernel-based estimators, which are also consistent. We study the efficiency of our new kernel-based procedure. We show that optimal modified kernel-based estimator converges to the integrated variance at the optimal rate, m^1/4, where m is the number of intraday returns.

Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise.HAC estimator, Long run variance estimator, Market frictions, Quadratic variation, Realised variance

Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise

This paper shows how to use realised kernels to carry out efficient feasible inference on the ex-post variation of underlying equity prices in the presence of simple models of market frictions. The issue is subtle with only estimators which have symmetric weights delivering consistent estimators with mixed Gaussian limit theorems. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem. Realised kernels can also be selected to (i) be analysed using endogenously spaced data such as that in databases on transactions, (ii) allow for market frictions which are endogenous, (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.